Short-term PV Power Prediction Based on Data Mining and Multi-kernel SVM

With the rapid increase of the total installed capacity of photovoltaic (PV) power generation, more accurate PV power prediction is required to ensure the safe and stable operation of the power grid. In order to improve the prediction accuracy of PV power when only irradiance and PV power data can be obtained, while other multi-source data such as temperature, precipitation and other meteorological data, are unavailable, the paper proposes a PV power prediction model based on data mining and the multi-kernel support vector machine (SVM). Firstly, the wavelet threshold denoising method is used to denoise the data of irradiance and PV power which contains many burrs and the large signal fluctuation. Then, the parameters are extracted by irradiance and power characteristic representation, which include six irradiance characteristic parameters and two power characteristic parameters. With the characteristic parameters, the similar days are selected by the data mining technology, a clustering algorithm using SOM and K-Means. Finally, the multi-kernel SVM is used for PV power prediction, where the multi-kernel function is used to deal with the distribution characteristics of data and improve the accuracy of PV power prediction. The experimental results show that the prediction accuracy can be improved by the wavelet threshold denoising and multi-kernel SVM. The high precision PV prediction results can also be obtained with the irradiance and PV power data only, and the PV prediction accuracy of multi-kernel SVM is higher than that of the single-kernel SVM and classical back propagation (BP) neural network.